Denecke, Kerstin; May, Richard; Rivera Romero, Octavio (2023). How Can Transformer Models Shape Future Healthcare: A Qualitative Study In: Giacomini, M. (ed.) Telehealth Ecosystems in Practice. Studies in Health Technology and Informatics: Vol. 309 (pp. 43-47). Amsterdam: IOS Press 10.3233/SHTI230736
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Transformer models have been successfully applied to various natural language processing and machine translation tasks in recent years, e.g. automatic language understanding. With the advent of more efficient and reliable models (e.g. GPT-3), there is a growing potential for automating time-consuming tasks that could be of particular benefit in healthcare to improve clinical outcomes. This paper aims at summarizing potential use cases of transformer models for future healthcare applications. Precisely, we conducted a survey asking experts on their ideas and reflections for future use cases. We received 28 responses, analyzed using an adapted thematic analysis. Overall, 8 use case categories were identified including documentation and clinical coding, workflow and healthcare services, decision support, knowledge management, interaction support, patient education, health management, and public health monitoring. Future research should consider developing and testing the application of transformer models for such use cases.
Item Type: |
Book Section (Book Chapter) |
|---|---|
Division/Institute: |
School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science |
Name: |
Denecke, Kerstin 0000-0001-6691-396X; May, Richard; Rivera Romero, Octavio and Giacomini, M. |
Subjects: |
R Medicine > R Medicine (General) T Technology > T Technology (General) |
ISBN: |
9781643684505 |
Series: |
Studies in Health Technology and Informatics |
Publisher: |
IOS Press |
Language: |
English |
Submitter: |
Kerstin Denecke |
Date Deposited: |
30 Oct 2023 15:34 |
Last Modified: |
01 Nov 2023 13:54 |
Publisher DOI: |
10.3233/SHTI230736 |
Uncontrolled Keywords: |
transformer models, deep learning, healthcare, applications |
ARBOR DOI: |
10.24451/arbor.20254 |
URI: |
https://arbor.bfh.ch/id/eprint/20254 |
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